Metadata-Version: 2.1
Name: ptflops
Version: 0.7.2.2
Summary: Flops counter for neural networks in pytorch framework
Author-email: Vladislav Sovrasov <sovrasov.vlad@gmail.com>
Maintainer-email: Vladislav Sovrasov <sovrasov.vlad@gmail.com>
License: MIT License
        
        Copyright (c) 2019 Vladislav Sovrasov
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/sovrasov/flops-counter.pytorch/
Project-URL: Documentation, https://github.com/sovrasov/flops-counter.pytorch/blob/master/README.md
Project-URL: Repository, https://github.com/sovrasov/flops-counter.pytorch.git
Project-URL: Bug Tracker, https://github.com/sovrasov/flops-counter.pytorch/issues
Project-URL: Changelog, https://github.com/sovrasov/flops-counter.pytorch/blob/master/CHANGELOG.md
Keywords: pytorch,cnn,transformer,tomatoes,Lobster Thermidor
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Provides-Extra: dev
Requires-Dist: flake8==3.8.1; extra == "dev"
Requires-Dist: flake8-import-order==0.18.1; extra == "dev"
Requires-Dist: isort==4.3.21; extra == "dev"
Requires-Dist: torchvision>=0.5.0; extra == "dev"
Requires-Dist: pytest==7.1.2; extra == "dev"
Requires-Dist: packaging; extra == "dev"

# Flops counting tool for neural networks in pytorch framework
[![Pypi version](https://img.shields.io/pypi/v/ptflops.svg)](https://pypi.org/project/ptflops/)
[![Build Status](https://travis-ci.com/sovrasov/flops-counter.pytorch.svg?branch=master)](https://travis-ci.com/sovrasov/flops-counter.pytorch)

This script is designed to compute the theoretical amount of multiply-add operations
in convolutional neural networks. It can also compute the number of parameters and
print per-layer computational cost of a given network.

Supported layers:
- Conv1d/2d/3d (including grouping)
- ConvTranspose1d/2d/3d (including grouping)
- BatchNorm1d/2d/3d, GroupNorm, InstanceNorm1d/2d/3d, LayerNorm
- Activations (ReLU, PReLU, ELU, ReLU6, LeakyReLU, GELU)
- Linear
- Upsample
- Poolings (AvgPool1d/2d/3d, MaxPool1d/2d/3d and adaptive ones)

Experimental support:
- RNN, LSTM, GRU (NLH layout is assumed)
- RNNCell, LSTMCell, GRUCell
- torch.nn.MultiheadAttention
- torchvision.ops.DeformConv2d
- visual transformers from [timm](https://github.com/huggingface/pytorch-image-models)

Requirements: Pytorch >= 1.1, torchvision >= 0.3

Thanks to @warmspringwinds for the initial version of script.

## Usage tips

- This tool doesn't take into account some of the `torch.nn.functional.*` and `tensor.*` operations. Therefore unsupported operations are
not contributing to the final complexity estimation. See `ptflops/pytorch_ops.py:FUNCTIONAL_MAPPING,TENSOR_OPS_MAPPING` to check supported ops.
- `ptflops` launches a given model on a random tensor and estimates amount of computations during inference. Complicated models can have several inputs, some of them could be optional. To construct non-trivial input one can use the `input_constructor` argument of the `get_model_complexity_info`. `input_constructor` is a function that takes the input spatial resolution as a tuple and returns a dict with named input arguments of the model. Next this dict would be passed to the model as a keyword arguments.
- `verbose` parameter allows to get information about modules that don't contribute to the final numbers.
- `ignore_modules` option forces `ptflops` to ignore the listed modules. This can be useful
for research purposes. For instance, one can drop all convolutions from the counting process
specifying `ignore_modules=[torch.nn.Conv2d]`.

## Install the latest version
From PyPI:
```bash
pip install ptflops
```

From this repository:
```bash
pip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git
```

## Example
```python
import torchvision.models as models
import torch
from ptflops import get_model_complexity_info

with torch.cuda.device(0):
  net = models.densenet161()
  macs, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True,
                                           print_per_layer_stat=True, verbose=True)
  print('{:<30}  {:<8}'.format('Computational complexity: ', macs))
  print('{:<30}  {:<8}'.format('Number of parameters: ', params))
```

## Citation
If ptflops was useful for your paper or tech report, please cite me:
```
@online{ptflops,
  author = {Vladislav Sovrasov},
  title = {ptflops: a flops counting tool for neural networks in pytorch framework},
  year = 2018-2023,
  url = {https://github.com/sovrasov/flops-counter.pytorch},
}
```

## Benchmark

### [torchvision](https://pytorch.org/vision/0.16/models.html)

Model                  | Input Resolution | Params(M) | MACs(G)
---                    |---               |---        |---
alexnet                | 224x224          | 61.10     | 0.72
convnext_base          | 224x224          | 88.59     | 15.43
densenet121            | 224x224          | 7.98      | 2.90
efficientnet_b0        | 224x224          | 5.29      | 0.41
efficientnet_v2_m      | 224x224          | 54.14     | 5.43
googlenet              | 224x224          | 13.00     | 1.51
inception_v3           | 224x224          | 27.16     | 2.86
maxvit_t               | 224x224          | 30.92     | 5.48
mnasnet1_0             | 224x224          | 4.38      | 0.33
mobilenet_v2           | 224x224          | 3.50      | 0.32
mobilenet_v3_large     | 224x224          | 5.48      | 0.23
regnet_y_1_6gf         | 224x224          | 11.20     | 1.65
resnet18               | 224x224          | 11.69     | 1.83
resnet50               | 224x224          | 25.56     | 4.13
resnext50_32x4d        | 224x224          | 25.03     | 4.29
shufflenet_v2_x1_0     | 224x224          | 2.28      | 0.15
squeezenet1_0          | 224x224          | 1.25      | 0.84
vgg16                  | 224x224          | 138.36    | 15.52
vit_b_16               | 224x224          | 86.57     | 17.60
wide_resnet50_2        | 224x224          | 68.88     | 11.45
